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Methods for evaluating gene expression from Affymetrix microarray datasets

Overview of attention for article published in BMC Bioinformatics, June 2008
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Title
Methods for evaluating gene expression from Affymetrix microarray datasets
Published in
BMC Bioinformatics, June 2008
DOI 10.1186/1471-2105-9-284
Pubmed ID
Authors

Ning Jiang, Lindsey J Leach, Xiaohua Hu, Elena Potokina, Tianye Jia, Arnis Druka, Robbie Waugh, Michael J Kearsey, Zewei W Luo

Abstract

Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
United States 2 1%
France 2 1%
Portugal 1 <1%
Chile 1 <1%
Italy 1 <1%
Germany 1 <1%
Finland 1 <1%
Malaysia 1 <1%
Other 2 1%
Unknown 146 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 25%
Student > Ph. D. Student 27 17%
Professor > Associate Professor 15 9%
Student > Master 15 9%
Student > Bachelor 14 9%
Other 31 19%
Unknown 18 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 72 45%
Biochemistry, Genetics and Molecular Biology 20 12%
Computer Science 14 9%
Mathematics 8 5%
Medicine and Dentistry 8 5%
Other 20 12%
Unknown 19 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 July 2012.
All research outputs
#20,160,460
of 22,669,724 outputs
Outputs from BMC Bioinformatics
#6,820
of 7,247 outputs
Outputs of similar age
#78,974
of 82,123 outputs
Outputs of similar age from BMC Bioinformatics
#37
of 38 outputs
Altmetric has tracked 22,669,724 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.